Real-time AI-enhanced Low Dose Fluoroscopy
Real-time AI-enhanced Low Dose Fluoroscopy
批准号:
10385142
负责人:
Enhao Gong
金额:
$13.22万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-09-25 至 2023-09-24
关键词:
AdoptedAnatomyArchitectureArtificial IntelligenceBreast Cancer Risk FactorCadaverChildClinicClinicalClinical ResearchComputer softwareData SetDevelopmentDoseExposure toFeedbackFluoroscopyGoalsHospitalsImageImage EnhancementImaging DeviceIndustrializationIonizing radiationLearningLimb structureMagnetic Resonance ImagingMalignant Childhood NeoplasmMalignant NeoplasmsMedicalMedical ImagingMethodsModelingMotivationOperating RoomsOperative Surgical ProceduresOrthopedicsPatientsPerformancePhasePositron-Emission TomographyProceduresRadiation Dose UnitRadiation exposureRecurrenceResearchRiskRoentgen RaysSmall Business Innovation Research GrantSpeedSpinalSpine surgerySurgeonSystemThyroid GlandTimeTrainingTranslatingVertebral columnVisualizationWomanWorkbasecancer riskdeep learningdenoisingexperienceimage guided interventionimage processingimage reconstructionimaging modalityimprovedinnovationmillisecondminimally invasivemodel designnoveloperationpreservationprocessing speedreal time modelsoftware developmentsuccesstool
中文摘要
项目总结
英文摘要
Project Summary
Motivation: Fluoroscopy is an indispensable tool for image-guided interventions in 50 million
surgeries performed annually in the US. It leverages ionizing radiation from x-rays to provide
surgeons with real-time, high-quality imaging feedback. Radiation exposure is harmful for both
the patients and surgeons. Repetitive patient exposure has been shown to double the risk of
breast cancer in women; meanwhile, surgeon exposure is also concerning. Orthopedic
surgeons are 5x more likely to develop cancer in their lifetime; spine surgeons receive up to 12x
more radiation exposure compared to other orthopedic surgeons. With minimally invasive
surgery becoming widely adopted in recent years, the use of fluoroscopy has greatly increased.
Surgeons rely on the navigation provided by fluoroscopy during these procedures, as they do
not have direct visualization of the anatomy. Therefore, reducing the radiation exposure from
fluoroscopy while maintaining high imaging quality is a high priority. In the past few years,
artificial intelligence (AI) methods have shown promising advances to improve the quality of
medical imaging. Subtle Medical Inc. has already received FDA clearance for its AI-based
software products to reduce the dose for PET by four times and improve image quality for MRI.
The motivation of this proposal is to translate our initial success to another imaging modality and
achieve low dose fluoroscopy.
Approach: This phase I SBIR project has three aims. Aim 1 is to develop AI software using
recurrent deep learning architecture to achieve 6x dose reduction for fluoroscopy. Aim 2 is to
design model pruning, kernel optimization, and high-performance inference frameworks to
achieve real-time processing. Finally, in Aim 3, we will evaluate both qualitatively and
quantitatively the developed software on phantoms and cadavers.
Significance: This work will enable six times lower dose fluoroscopy. The completion of the
project will have wide impact to greatly reduce radiation exposure in the operating room, hence
reducing the risk of cancer development for both patients and clinicians.
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会议论文
Low- and Zero-dose Contrast-enhanced MRI Using Deep Learning
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批准号:10225646
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项目类别:
-
资助金额:$75.57万
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财政年份:2020
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负责人:Enhao Gong
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依托单位:
海外基金